Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method for matching advertisements to target audiences, comprising: aggregating a dataset including a plurality of records associated with a plurality of mobile devices, each of the one or more records including a device identifier, a location identifier, and a time associated with the mobile device; generating a plurality of audience polygons based on an audience raster associated with the aggregated dataset, wherein the audience raster is representative of a distribution of the plurality of mobile devices that satisfy a target audience condition over a plurality of sub-regions of a geographic area for a given one of a set of time periods, and wherein each audience polygon encloses a respective geographic region within the geographic area and is associated with a respective time period and respective target audience; receiving an advertisement request from a remote display system having a display at a particular geographic location, the request including an advertisement parameter that identifies a first target time period; selecting a first audience polygon from the plurality of audience polygons, the selecting based on the respective geographic region of the first audience polygon and the identified first target time period wherein the first audience polygon encloses the geographic location of the display and a time period associated with the first audience polygon is inclusive of the identified first target time period; determining a first target audience using the first audience polygon; selecting an advertisement associated with the first target audience; and providing the advertisement to the remote display system for presentation on the display at the particular geographic location during the first target time period.
This invention relates to targeted digital advertising, specifically a method for matching advertisements to audiences based on mobile device location data. The system aggregates records from multiple mobile devices, each containing a device identifier, location, and timestamp. These records are used to generate audience polygons, which are geographic regions representing the distribution of devices meeting specific audience criteria over different time periods. The polygons are derived from an audience raster, a grid-based representation of device density across a geographic area. When an advertisement request is received from a display system at a specific location, the system selects an audience polygon that encompasses the display's location and includes the requested time period. The polygon defines the target audience, and an advertisement matching this audience is selected and sent to the display for presentation during the specified time. This approach enables dynamic, location- and time-specific ad targeting by leveraging historical mobile device movement patterns. The method improves ad relevance by ensuring advertisements are shown to audiences most likely to be present at the display location during the requested time.
2. The computer-implemented method of claim 1 , further comprising: receiving a dataset including a plurality of records from one or more cell service providers that are associated with mobile device users, each record including a respective timestamp and geographic identifier; inferring a plurality of characteristics about one or more of the mobile device users by comparing timestamps and geographic identifiers in each record to a set of rules that map inferred characteristics to specific combinations of geographic and temporal conditions; and generating one or more audience polygons using the inferred plurality of characteristics.
This invention relates to analyzing mobile device user data from cell service providers to infer user characteristics and generate audience polygons for targeted marketing or analytics. The method involves receiving a dataset containing records from mobile devices, where each record includes a timestamp and geographic identifier. By comparing these timestamps and locations against predefined rules, the system infers characteristics about users, such as their likely activities, interests, or behaviors, based on patterns in their movement and timing. For example, frequent visits to a gym at specific times may indicate fitness-related interests. The inferred characteristics are then used to create audience polygons—geographic areas where users with specific traits are concentrated. These polygons can be used for targeted advertising, demographic analysis, or other applications where understanding user behavior in specific locations is valuable. The method leverages temporal and spatial data to derive insights without requiring explicit user input, providing a scalable way to segment audiences based on inferred behavior patterns.
3. The computer-implemented method of claim 2 , wherein generating one or more audience polygons using the inferred plurality of characteristics, comprises: determining a baseline group of records in the dataset having timestamps within a first time period; generating a baseline raster using the baseline group of records, the baseline raster describing a baseline distribution of mobile device users across a geographic region which is subdivided into a plurality of sub-regions; determining a subset of the baseline group of records that are associated with mobile device users that each have in common a first inferred characteristic of the inferred plurality of characteristics; generating a second audience raster using the subset of the baseline group of records, the second audience raster describing an audience distribution across the plurality of sub-regions for the mobile device users associated with the subset of the baseline group of records; generating a normalized audience raster using the second audience raster and the baseline raster, the normalized audience raster describing a normalized audience distribution across the plurality of sub-regions and indicating affinities describing probabilities that mobile device users having the first characteristic are found within each of the plurality of sub-regions; and generating the one or more audience polygons using the normalized audience raster.
This invention relates to analyzing mobile device user data to generate geographic audience distributions based on inferred characteristics. The method addresses the challenge of identifying and visualizing where specific user groups are concentrated within a geographic area, enabling targeted insights for marketing, urban planning, or other applications. The process begins by selecting a baseline group of mobile device records from a dataset, filtered to include only those with timestamps within a specified time period. These records are used to create a baseline raster—a grid representation of the geographic region divided into sub-regions—showing the general distribution of mobile device users across the area. Next, a subset of the baseline group is identified, consisting of records associated with users sharing a specific inferred characteristic (e.g., age, interests, or behavior). A second raster is generated from this subset, representing the distribution of users with that characteristic. This raster is then normalized against the baseline raster to produce a normalized audience raster, which highlights affinities—probabilities that users with the characteristic are present in each sub-region. Finally, the normalized raster is used to generate one or more audience polygons, which are geographic boundaries enclosing areas where the characteristic is most prevalent. This allows for precise mapping of audience concentrations, enabling targeted analysis or interventions. The method can be repeated for multiple characteristics to create a comprehensive geographic profile of different user groups.
4. The computer-implemented method of claim 3 , wherein generating the one or more audience polygons using the normalized audience raster comprises: identifying regions of the plurality of regions of the normalized audience raster that have different affinities; generating the one or more audience polygons using the identified regions, each audience polygon associated with the first time period and one of the different affinities; and storing the one or more audience polygons as part of the plurality of audience polygons.
This invention relates to audience analysis in digital media, specifically methods for generating and storing audience polygons based on normalized audience data. The problem addressed is the need to efficiently segment and categorize audience data over time to better understand viewer behavior and preferences. The method involves processing a normalized audience raster, which is a grid representation of audience data where each cell contains an affinity value indicating the likelihood of audience presence or engagement. The method identifies distinct regions within this raster that exhibit different affinity levels, which may correspond to varying degrees of audience interest or activity. These regions are then converted into audience polygons, each representing a contiguous area with a specific affinity value. Each polygon is associated with a particular time period, allowing for temporal analysis of audience behavior. The generated audience polygons are stored as part of a larger dataset, enabling further analysis, visualization, or integration with other data sources. This approach facilitates the identification of patterns, trends, and shifts in audience engagement over time, supporting applications in advertising, content recommendation, and audience targeting. The method ensures that the audience data is structured in a way that preserves spatial and temporal relationships, enhancing the accuracy and utility of the analysis.
5. The computer-implemented method of claim 1 , wherein the display is statically mounted at the particular geographic location and is configured to present the advertisement to multiple members of the target audience.
This invention relates to digital advertising systems designed to target specific audiences based on geographic location. The problem addressed is the inefficiency of traditional advertising methods that do not dynamically adapt to the real-time presence of target audiences in specific locations. The solution involves a computer-implemented method that identifies a target audience for an advertisement, determines their geographic location, and displays the advertisement on a screen located at that specific geographic location. The system uses data such as user profiles, device locations, or other contextual information to identify the target audience. The advertisement is then presented on a display that is statically mounted at the determined geographic location, ensuring visibility to multiple members of the target audience who are present in that area. The method may also include tracking the effectiveness of the advertisement by monitoring user interactions or engagement metrics. This approach improves advertising relevance and efficiency by ensuring that ads are shown to the right audience at the right location.
6. The computer-implemented method of claim 1 , further comprising: receiving a second advertisement request from an advertiser, the request identifying a second target audience whose members have a particular characteristic in common with each other and identifying a second target time period; selecting a second audience polygon, the selecting based in part on a time period associated with the second audience polygon being inclusive of the second target time period and the target audience associated with the second audience polygon matching the second target audience; and providing the second audience polygon to the advertiser, wherein the advertiser selects and serves an advertisement to a second display of the remote display system based in part on the second display being located at a geographic region enclosed by the second audience polygon.
This invention relates to targeted digital advertising systems that use geographic audience polygons to deliver advertisements to specific groups of users based on their location and shared characteristics. The problem addressed is the need for advertisers to efficiently target audiences in specific geographic areas during defined time periods, ensuring ads are served to relevant users while minimizing wasted impressions. The method involves receiving an advertisement request from an advertiser, which specifies a target audience with a shared characteristic and a target time period. A system selects an audience polygon—a geographic boundary—that matches the target audience and includes the target time period. The polygon is provided to the advertiser, who then serves an advertisement to displays within the polygon's geographic region during the specified time. This ensures ads are shown to users who meet the advertiser's criteria, improving ad relevance and campaign efficiency. The method can be extended to handle multiple requests, where a second advertisement request is processed similarly. A second audience polygon is selected based on its associated time period matching the second target time period and its audience matching the second target audience. The advertiser then serves ads to displays within the second polygon's geographic region. This approach allows dynamic, location-based ad targeting, optimizing ad delivery for different audiences and time frames.
7. The computer-implemented method of claim 1 , wherein multiple audience polygons, of the plurality of audience polygons, enclose a same geographic region but are associated with different time periods and different target audiences.
This invention relates to geographic audience targeting in digital advertising, specifically improving the precision of audience segmentation by time and location. The problem addressed is the inefficiency of traditional geographic targeting methods, which often apply a single audience definition to an entire area without accounting for temporal or demographic variations within that region. The method involves creating multiple audience polygons that overlap or enclose the same geographic region but are differentiated by distinct time periods and target audience criteria. Each polygon represents a unique combination of location, time, and audience attributes, allowing for more granular and context-aware ad targeting. For example, a single city block might have one polygon targeting commuters during morning rush hour and another targeting shoppers during weekend afternoons, each with tailored ad content. The system dynamically generates these polygons based on historical data, real-time analytics, and user behavior patterns. By segmenting audiences in this way, advertisers can deliver more relevant ads, reducing wasted impressions and improving campaign performance. The method also supports real-time adjustments to polygon definitions as new data becomes available, ensuring continuous optimization. This approach enhances the precision of location-based advertising by incorporating temporal and demographic dimensions, addressing limitations of static geographic targeting. The solution is particularly useful for industries like retail, transportation, and local services where audience behavior varies significantly by time and context.
8. A non-transitory computer-readable storage medium storing executable computer program instructions for method for matching advertisements to target audiences, the instructions executable to perform steps comprising: aggregating a dataset including a plurality of records associated with a plurality of mobile devices, each of the one or more records including a device identifier, a location identifier, and a time associated with the mobile device; generating a plurality of audience polygons based on an audience raster associated with the aggregated dataset, wherein the audience raster is representative of a distribution of the plurality of mobile devices that satisfy a target audience condition over a plurality of sub-regions of a geographic area for a given one of a set of time periods, and wherein each audience polygon encloses a respective geographic region within the geographic area and is associated with a respective time period and respective target audience; receiving an advertisement request from a remote display system having a display at a particular geographic location, the request including an advertisement parameter that identifies a first target time period; selecting a first audience polygon from the plurality of audience polygons, the selecting based on the respective geographic region of the first audience polygon and the identified first target time period wherein the first audience polygon encloses the geographic location of the display and a time period associated with the first audience polygon is inclusive of the identified first target time period; determining a first target audience using the first audience polygon; selecting an advertisement associated with the first target audience; and providing the advertisement to the remote display system for presentation on the display at the particular geographic location during the first target time period.
This invention relates to targeted digital advertising, specifically a system for matching advertisements to audiences based on mobile device location data. The problem addressed is the need for more precise and contextually relevant ad delivery by leveraging real-time or historical mobile device movement patterns. The system aggregates data from multiple mobile devices, including device identifiers, locations, and timestamps. This data is processed to generate an audience raster—a grid-based representation of device distributions across geographic sub-regions over specific time periods. From this raster, audience polygons are created, each defining a geographic area where devices meeting certain criteria (e.g., demographics, behavior) are concentrated during a given time window. When an ad request is received from a display system at a specific location, the system selects the most relevant audience polygon based on the display’s geographic proximity and the request’s target time period. The polygon’s associated audience data determines the ad’s target demographic, and a matching advertisement is selected and sent to the display for presentation during the specified time. This approach improves ad targeting by dynamically aligning content with audience presence patterns in specific locations and timeframes, enhancing relevance and engagement.
9. The computer-readable medium of claim 8 , further comprising: receiving a dataset including a plurality of records from one or more cell service providers that are associated with mobile device users, each record including a respective timestamp and geographic identifier; inferring a plurality of characteristics about one or more of the mobile device users by comparing timestamps and geographic identifiers in each record to a set of rules that map inferred characteristics to specific combinations of geographic and temporal conditions; and generating one or more audience polygons using the inferred plurality of characteristics.
This invention relates to analyzing mobile device data to infer user characteristics and generate audience segments. The problem addressed is the need to derive meaningful insights from mobile device location data to create targeted audience groups for marketing or analytics purposes. The system receives a dataset from cell service providers, containing records associated with mobile device users. Each record includes a timestamp and a geographic identifier, such as GPS coordinates or cell tower data. The system processes these records by comparing the timestamps and geographic identifiers against a predefined set of rules. These rules map specific combinations of geographic and temporal conditions to inferred characteristics, such as user behavior patterns, demographics, or interests. For example, frequent visits to a gym at specific times may infer fitness-related interests, while repeated visits to business districts during work hours may suggest a professional profile. The system then uses these inferred characteristics to generate audience polygons, which are geographic boundaries representing groups of users sharing similar traits. These polygons can be used for targeted advertising, demographic analysis, or other applications requiring location-based audience segmentation. The invention improves upon prior methods by automating the inference of user characteristics from raw mobile data, enabling more precise and scalable audience targeting.
10. The computer-readable medium of claim 9 , wherein generating one or more audience polygons using the inferred plurality of characteristics, comprises: determining a baseline group of records in the dataset having timestamps within a first time period; generating a baseline raster using the baseline group of records, the baseline raster describing a baseline distribution of mobile device users across a geographic region which is subdivided into a plurality of sub-regions; determining a subset of the baseline group of records that are associated with mobile device users that each have in common a first inferred characteristic of the inferred plurality of characteristics; generating a second audience raster using the subset of the baseline group of records, the second audience raster describing an audience distribution across the plurality of sub-regions for the mobile device users associated with the subset of the baseline group of records; generating a normalized audience raster using the second audience raster and the baseline raster, the normalized audience raster describing a normalized audience distribution across the plurality of sub-regions and indicating affinities describing probabilities that mobile device users having the first characteristic are found within each of the plurality of sub-regions; and generating the one or more audience polygons using the normalized audience raster.
This invention relates to analyzing mobile device user data to generate geographic audience distributions based on inferred characteristics. The problem addressed is the need to identify and visualize patterns of mobile device users with specific traits across geographic regions. The solution involves processing timestamped mobile device records to create audience polygons that highlight areas with higher concentrations of users sharing a particular characteristic. The method begins by selecting a baseline group of records within a specified time period. These records are used to generate a baseline raster, which maps the general distribution of mobile device users across a geographic area divided into sub-regions. Next, a subset of these records is filtered to include only users sharing a specific inferred characteristic (e.g., age, interests, or behavior). A second raster is generated from this subset, representing the distribution of users with that characteristic. The baseline and subset rasters are then combined to produce a normalized audience raster, which adjusts for overall user density and highlights areas where the characteristic is more prevalent. Finally, the normalized raster is converted into one or more audience polygons, which are geographic boundaries enclosing regions where the characteristic is significantly represented. This approach enables targeted analysis of user demographics or behaviors in specific locations.
11. The computer-readable medium of claim 10 , wherein generating the one or more audience polygons using the normalized audience raster comprises: identifying regions of the plurality of regions of the normalized audience raster that have different affinities; generating the one or more audience polygons using the identified regions, each audience polygon associated with the first time period and one of the different affinities; and storing the one or more audience polygons as part of the plurality of audience polygons.
This invention relates to audience analysis in digital media, specifically improving the segmentation of audience data for targeted advertising or content delivery. The problem addressed is the difficulty in accurately identifying and grouping audience segments based on their affinities (e.g., interests, behaviors) over time, particularly when working with large-scale spatial or temporal data. The invention involves processing a normalized audience raster—a grid-like representation of audience data where each cell contains affinity values for a given time period. The system identifies regions within this raster that exhibit distinct affinity patterns, then converts these regions into audience polygons. Each polygon represents a group of users with similar affinities during a specific time period. These polygons are stored as part of a larger dataset for further analysis or targeting. The process ensures that audience segments are dynamically updated based on real-time or historical data, allowing for more precise audience targeting. By segmenting the raster into polygons, the system enables efficient querying and visualization of audience distributions, improving decision-making for advertisers or content providers. The invention enhances traditional audience analysis by automating the segmentation process and providing structured, time-specific audience groups.
12. The computer-readable medium of claim 8 , further comprising: receiving a second advertisement request from an advertiser, the request identifying a second target audience whose members have a particular characteristic in common with each other and identifying a second target time period; selecting a second audience polygon, the selecting based in part on a time period associated with the second audience polygon being inclusive of the second target time period and the target audience associated with the second audience polygon matching the second target audience; and providing the second audience polygon to the advertiser, wherein the advertiser selects and serves an advertisement to a second display of the remote display system based in part on the second display being located at a geographic region enclosed by the second audience polygon.
This invention relates to targeted digital advertising systems that use geographic audience polygons to deliver advertisements to specific groups of users based on their location and shared characteristics. The problem addressed is the need for advertisers to efficiently target audiences in specific geographic areas during particular time periods, ensuring ads are shown to the most relevant viewers. The system involves creating audience polygons that define geographic regions where users share a common characteristic, such as demographics or behavior, and are active during a specific time period. When an advertiser submits a request for an advertisement campaign, they specify a target audience with a shared trait and a target time period. The system then selects an audience polygon that matches the advertiser's criteria, meaning the polygon's associated time period includes the target time and its audience matches the target group. The advertiser receives the polygon and uses it to serve ads to displays within the enclosed geographic region, ensuring the ads reach the intended audience during the specified time. This approach improves ad targeting by leveraging predefined audience polygons, reducing the need for real-time audience analysis and ensuring ads are delivered to the right users at the right time.
13. The computer-readable medium of claim 8 , wherein multiple audience polygons, of the plurality of audience polygons, enclose a same geographic region but are associated with different time periods and different target audiences.
This invention relates to geographic audience targeting systems, specifically for dynamically defining and managing audience polygons that represent geographic regions of interest for targeted content delivery. The problem addressed is the need to efficiently segment geographic areas for different audiences and time periods, allowing for precise and time-sensitive content targeting. The system involves creating a plurality of audience polygons, each representing a distinct geographic region. These polygons can overlap or be nested, enabling flexible geographic segmentation. The key innovation is that multiple audience polygons may enclose the same geographic region but are associated with different time periods and different target audiences. This allows the same physical area to be targeted differently depending on temporal factors and audience demographics, improving the granularity and relevance of content delivery. The system dynamically adjusts these polygons based on real-time data, such as audience movement or behavior, ensuring that the targeting remains accurate and up-to-date. This approach enhances the efficiency of geographic audience targeting by reducing redundancy and improving precision in content distribution. The invention is particularly useful in advertising, public safety, and location-based services where time-sensitive and audience-specific targeting is critical.
14. An audience matching system comprising: a processor; and a non-transitory computer-readable storage medium coupled to the processor, the computer-readable storage medium including instructions that, when executed by the processor, cause the system to perform steps comprising: aggregating a dataset including a plurality of records associated with a plurality of mobile devices, each of the one or more records including a device identifier, a location identifier, and a time associated with the mobile device; generating a plurality of audience polygons based on an audience raster associated with the aggregated dataset, wherein the audience raster is representative of a distribution of the plurality of mobile devices that satisfy a target audience condition over a plurality of sub-regions of a geographic area for a given one of a set of time periods, and wherein each audience polygon encloses a respective geographic region within the geographic area and is associated with a respective time period and a respective target audience; receiving an advertisement request from a remote display system having a display at a particular geographic location, the request including an advertisement parameter that identifies a first target time period; selecting a first audience polygon from the plurality of audience polygons, the selecting based on the respective geographic region of the first audience polygon and the identified first target time period wherein the first audience polygon encloses the geographic location of the display and a time period associated with the first audience polygon is inclusive of the identified first target time period; determining a first target audience using the first audience polygon; selecting an advertisement associated with the first target audience; and providing the advertisement to the remote display system for presentation on the display at the particular geographic location during the first target time period.
This system relates to audience targeting for digital advertising, specifically using mobile device location data to optimize ad placement. The problem addressed is the inefficiency of traditional ad targeting methods that do not dynamically adapt to real-time audience distributions. The system aggregates mobile device data, including device identifiers, locations, and timestamps, to create an audience raster—a grid-based representation of where target audiences are located over time. From this, it generates audience polygons, which are geographic regions where specific audience segments are concentrated during particular time periods. When an ad request is received from a display system at a specific location, the system selects the most relevant audience polygon based on geographic proximity and time alignment. It then determines the target audience for that polygon and selects an appropriate advertisement to display during the specified time period. This approach ensures ads are shown to the most relevant audiences at optimal times, improving ad effectiveness and engagement. The system dynamically adjusts to changing audience patterns, making it more adaptable than static targeting methods.
15. The system of claim 14 , wherein the instructions, when executed by the processor, further cause the system to perform steps comprising: receiving a dataset including a plurality of records from one or more cell service providers that are associated with mobile device users, each record including a respective timestamp and geographic identifier; inferring a plurality of characteristics about one or more of the mobile device users by comparing timestamps and geographic identifiers in each record to a set of rules that map inferred characteristics to specific combinations of geographic and temporal conditions; and generating one or more audience polygons using the inferred plurality of characteristics.
The system processes mobile device data from cell service providers to analyze user behavior and generate audience segments. The data includes records with timestamps and geographic identifiers for mobile device users. The system infers user characteristics by comparing these records against predefined rules that link specific geographic and temporal patterns to inferred traits. For example, frequent visits to certain locations at particular times may indicate employment status, lifestyle preferences, or other demographic attributes. The system then uses these inferred characteristics to create audience polygons, which are geographic boundaries representing groups of users sharing similar inferred traits. This enables targeted marketing, demographic analysis, or other applications where understanding user behavior patterns is valuable. The system automates the extraction of meaningful insights from raw mobile device data, providing actionable audience segmentation without manual analysis. The inferred characteristics may include lifestyle habits, travel patterns, or other behavioral attributes derived from the temporal and spatial data. The audience polygons can be used for location-based services, advertising, or urban planning by identifying clusters of users with shared inferred traits.
16. The system of claim 15 , wherein generating one or more audience polygons using the inferred plurality of characteristics, comprises: determining a baseline group of records in the dataset having timestamps within a first time period; generating a baseline raster using the baseline group of records, the baseline raster describing a baseline distribution of mobile device users across a geographic region which is subdivided into a plurality of sub-regions; determining a subset of the baseline group of records that are associated with mobile device users that each have in common a first inferred characteristic of the inferred plurality of characteristics; generating a second audience raster using the subset of the baseline group of records, the second audience raster describing an audience distribution across the plurality of sub-regions for the mobile device users associated with the subset of the baseline group of records; generating a normalized audience raster using the second audience raster and the baseline raster, the normalized audience raster describing a normalized audience distribution across the plurality of sub-regions and indicating affinities describing probabilities that mobile device users having the first characteristic are found within each of the plurality of sub-regions; and generating the one or more audience polygons using the normalized audience raster.
This system analyzes mobile device data to generate audience polygons representing geographic distributions of users with specific inferred characteristics. The technology addresses the challenge of identifying and visualizing where users with particular traits are concentrated within a geographic area. The process begins by selecting a baseline group of mobile device records from a dataset, filtered to a specific time period. These records are used to create a baseline raster, a grid-based representation showing the general distribution of mobile device users across subdivided geographic regions. Next, a subset of these records is isolated, focusing only on users sharing a specific inferred characteristic (e.g., age, interests, or behavior). A second raster is generated from this subset, depicting the distribution of users with that characteristic. The system then normalizes this distribution by comparing it to the baseline raster, producing a normalized audience raster. This raster highlights affinities—probabilities that users with the characteristic are present in each sub-region. Finally, the normalized raster is used to generate audience polygons, which are geographic boundaries outlining areas with high concentrations of the target audience. This approach enables precise audience segmentation and geographic targeting for applications like marketing, urban planning, or public policy.
17. The system of claim 16 , wherein generating the one or more audience polygons using the normalized audience raster comprises: identifying regions of the plurality of regions of the normalized audience raster that have different affinities; generating the one or more audience polygons using the identified regions, each audience polygon associated with the first time period and one of the different affinities; and storing the one or more audience polygons as part of the plurality of audience polygons.
This invention relates to audience analysis in media or advertising, specifically improving the segmentation of audience data for targeted content delivery. The system processes audience data to generate polygons representing distinct audience segments with varying affinities, enabling more precise targeting over time. The system first creates a normalized audience raster, a grid-like representation of audience data where each cell contains normalized values indicating audience affinity. The raster is divided into multiple regions, each corresponding to a geographic area or demographic segment. The system then identifies regions within this raster that exhibit different audience affinities, such as varying levels of engagement or interest in specific content. Using these identified regions, the system generates one or more audience polygons, where each polygon corresponds to a distinct affinity level and is associated with a specific time period. These polygons are stored as part of a larger dataset of audience polygons, allowing for dynamic segmentation and analysis. The polygons can be used to tailor content delivery, optimize advertising campaigns, or analyze audience behavior over time. This approach enhances traditional audience segmentation by dynamically adjusting to changes in affinity, improving the accuracy and relevance of targeted media distribution.
18. The system of claim 14 , wherein the display is statically mounted at the particular geographic location and is configured to present the advertisement to multiple members of the target audience.
This invention relates to an advertising system designed for presenting targeted advertisements to specific audiences at fixed geographic locations. The system includes a display device that is statically mounted at a particular location, such as a public space, transit hub, or commercial area, to ensure visibility to multiple members of a predefined target audience. The display is configured to present advertisements tailored to the demographic, behavioral, or contextual characteristics of the audience in that location. The system may also incorporate sensors or data sources to gather real-time information about the audience, such as foot traffic patterns or environmental conditions, to optimize ad delivery. Additionally, the system may include a content management module that dynamically selects and schedules advertisements based on predefined criteria, ensuring relevance and engagement. The static mounting of the display ensures consistent exposure to the target audience, enhancing the effectiveness of the advertising campaign. The system may also integrate with external data sources, such as weather services or event calendars, to further refine ad targeting. The overall goal is to provide a highly localized and efficient advertising solution that maximizes reach and impact within a specific geographic area.
19. The system of claim 14 , wherein the instructions, when executed by the processor, further cause the system to perform steps comprising: receiving a second advertisement request from an advertiser, the request identifying a second target audience whose members have a particular characteristic in common with each other and identifying a second target time period; selecting a second audience polygon, the selecting based in part on a time period associated with the second audience polygon being inclusive of the second target time period and the target audience associated with the second audience polygon matching the second target audience; and providing the second audience polygon to the advertiser, wherein the advertiser selects and serves an advertisement to a second display of the remote display system based in part on the second display being located at a geographic region enclosed by the second audience polygon.
This invention relates to targeted digital advertising systems that use geographic audience polygons to deliver advertisements to specific audiences based on their location and characteristics. The problem addressed is the need for advertisers to efficiently target audiences in specific geographic regions during particular time periods, ensuring that advertisements are displayed to the intended audience at the right time and place. The system receives an advertisement request from an advertiser, which specifies a target audience with shared characteristics and a target time period. The system then selects an audience polygon—a geographic boundary—that encompasses the target audience and aligns with the target time period. The audience polygon is provided to the advertiser, who uses it to serve advertisements to displays located within the polygon's geographic region during the specified time. This ensures that advertisements are shown to the intended audience in the correct location and time frame. The system can also handle multiple requests, allowing advertisers to define different target audiences and time periods. For each request, the system selects an appropriate audience polygon that matches the specified criteria, ensuring precise geographic and temporal targeting. This approach improves advertising efficiency by reducing wasted impressions and increasing relevance for viewers.
20. The system of claim 14 , wherein multiple audience polygons, of the plurality of audience polygons, enclose a same geographic region but are associated with different time periods and different target audiences.
This audience matching system processes aggregated mobile device location and time data to generate "audience polygons." Each polygon defines a specific geographic region, a time period, and a target audience, derived from the distribution of mobile devices satisfying certain conditions. When an advertisement request is received from a display at a particular location and time, the system selects an appropriate audience polygon that encompasses the display's location and time period, determines the target audience, and serves a relevant ad. A key feature is that the system can generate and utilize multiple audience polygons that enclose the *same geographic region*. However, these distinct polygons are associated with *different time periods* or *different target audiences*, allowing for fine-grained targeting within the same physical space. ERROR (embedding): Error: Failed to save embedding: Could not find the 'embedding' column of 'patent_claims' in the schema cache
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February 4, 2020
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